Everyone's stacking AI experts into "councils" right now. Here's what nobody mentions: most of them produce blander advice than a single good prompt. I've been building multi-agent systems for a while, and the council pattern is seductive. Load six marketing legends, let them debate, synthesize the genius. Three things I learned the hard way: 1. Councils regress to the mean. Put Cialdini, Godin, and Vaynerchuk in a room and "synthesize" their answers and you get generic marketing advice wearing three nametags. The fix isn't a better synthesizer. Stop resolving the disagreement. Let the tension stand and make one agent own the call. 2. The debate is where your budget dies. Distilling a book into a tight skill file is cheap. Having agents argue in real time is not. If "minimal tokens" is your pitch, the preprocessing is doing the work and the roundtable is the luxury. 3. It doesn't make the model smarter. Cold Claude already does a soft version of all of this. What the structure buys you is named, sharp, predictable behavior. Say that honestly — the moment you claim it makes the AI "smarter," you've oversold it. None of this means don't build councils. It means build them with your eyes open. The real test for any council: do your experts actually disagree, or do they just agree in different vocabulary? If it's the second, you built one expert and gave it six hats. (Riffing off the systems thread from @Curtis Hays that @David Vogel highlighted for us and the 'systems' build — good work worth pressure-testing.)

(and a small change to how we run these) Hello everyone!! 👋 First, the honest bit. This one is landing later than Monday, and on purpose. Two things got us here. One, a lot more of you are submitting now. If I am going to really sit with every entry and give it a proper look, a weekend is not enough. This round I went through all of them, watched the videos, opened the repos, the full pass. That takes time and I would rather do it right than rush it. Two, I could feel a few of you running hot. Weekly is a sprint, and burnout was starting to creep in for some. So we are moving to bi-weekly. More room to build, more room to breathe, and the time for me to actually review the work the way it deserves. 🎥 Quick word on the videos. They were a step up this round. Some of the animated walkthroughs and live demos were a genuine pleasure to watch, and yes, I weigh them. A clean demo that shows the thing actually working makes a real difference. However I don't want that to ALWAYS be a requirement. Also you will notice the Heavy hitters that you usually see up here are not currently, some posted late and I decided to let the new entries and first timers also have a chance as well! But certainly, check the original post as every submission has something for you to learn from : 💰 Competition 7 ➖➖➖ 🛠️ A FEW THAT STOOD OUT (in no order, and if you didn't make it, it doesn't mean yours wasn't great) The Pipeline Operator — @Jayden Forshee Runs a whole sales pipeline. Paste a lead and it grades it, writes the outreach, and moves the card itself. The live board where you watch cards move on their own, sat right next to a normal chatbot, was one of the clearest ways anyone has shown what an operator actually is. https://github.com/griffainai/studio-pipeline-operator Board: https://pipeline-operator.vercel.app/board

Hey Clief Notes Community! Jake built this community on one premise: skills over agents. I just wrote a paper proving why he's right. The paper is called "The AI Skill Gap Isn't About AI: The Case for Language Arts as the Foundation of Generative AI Interaction & Engagement," and the argument is simple: The tools don't determine output quality. The human operating them does. Specifically: their ability to think clearly, structure that thinking into language, and communicate it precisely enough that the model can actually work with it. That's not a prompting skill. That's a Language Arts skill. I built a framework around it, seven layers from Structured Thought at the core to Adaptive Transfer at the outer edge, and grounded it in 67 empirical studies. If you're here, you already know the human is the variable. This is the research that says it out loud. This is the thinking and application that landed me TWO promotions in my day job and has generated mass interest in the education industry in my consulting endeavors. Would love to hear how this maps to what you're seeing in your own AI environments.

ICM's premise is that structure replaces orchestration: folders and markdown carry each stage's context, and one agent reads the right files at the right moment. The paper assumes that agent is capable. I wanted to see if ICM holds when the agent is a small local model you run yourself. It does, by leaning on two things ICM already gives you. Stage-scoped context becomes injection. In ICM the agent roams the workspace and opens what it needs. A model served through Ollama is not that agent. It is an inference endpoint that takes a prompt and returns text, with no file access and no navigation loop, so it cannot roam the folders at all. The engine reads the files and injects each stage's context into the prompt instead. Same principle, each stage sees only what it needs, delivered by code rather than fetched by the model. "Scripts handle what doesn't need AI" becomes an oracle per stage. ICM keeps the mechanical work out of the model. I extend that one step: every generative stage is checked by a deterministic oracle (for code, the compiler and its tests), because a small model proposes well but can't verify itself. Reliability stays in the structure, not the model. That is the whole port. One stage one job, plain-text artifacts, factory vs product, human-reviewable files: all carry over unchanged. ICM and MCP stay complementary, as the paper notes, with the folder structure deciding context and the stage's tools exposed over MCP. The result is a frontier-free assistant: the same methodology, running on hardware you own, for tasks that are narrow and checkable. Two repos, MIT, pure stdlib (you bring Ollama): - Rust coding assistant: https://github.com/CurtisSlone/ICM-Local-Model-Rust-Coding-Assistant - Reusable base to make your own: https://github.com/CurtisSlone/ICM-Local-Model-Base I also have a coffee test example that is way more simple than the coding assistant.

Hi Clief Notes Community! If you have not met me yet, my name is Bas and I am one of the members of our community! ⭐EDIT* Apparently there is a vote going on whether or not I should change my profile picture. https://www.skool.com/cliefnotes/petition-to-change-bas-profile-photo?p=abbdd137 Clief Notes is a very special place to me and I am always trying to bring us value so we can learn, grow and win together! 🤓💪🏆 So, I have already shared the Praxis Library a 255-page tribute to prompt engineering and AI literacy. After a recent post @Curtis Hays made about me, and after I wiped the tears from my eyes, I started to build something new, and I am building that something for us. Curtis Post You all joke that I'm a bot. 🤖 So I made one. Meet Bas 2.0, the curator of a new work in progress library at askbas.com. (Coming Soon page is up) I can't always be in the thread the second you get stuck, so I built a version of me that can be. Tell him what you're after and he'll pull it off the shelf for you, day or night. 📝NOTE: Not on the shelf yet? Bas 2.0 will open a search in the site and look up the ask with you, and he will send me a notification so we can look into what you wanted to see and add it to the pages of the library for others who could learn from the lesson. 📚 What's on the shelves: - 🌐A working library of AI Knowledge, ICM, context engineering, model tutorials, and multi-agent systems. - 🏫 Learning paths for wherever you are: a Beginner's Path, a Practitioner's Path, and an ICM Deep Dive. - 🤝 A hands-on how-to library, from Claude Code, Co-Work, and Design to ChatGPT, Goals, and Codex all the way through to Skills to building your own agents. - 📝 Lessons built to make this click, not just to fill a page.